M2

Report

1 Define functions

## Define function that recodes to numeric, but watches out to coercion to not introduce NAs
colstonumeric <- function(df){
  tryCatch({
    df_num <- as.data.frame(
      lapply(df,
             function(x) { as.numeric(as.character(x))})) 
  },warning = function(stop_on_warning) {
    message("Stoped the execution of numeric conversion: ", conditionMessage(stop_on_warning))
  }) 
}
##
## Define function that reverse codes items
ReverseCode <- function(df, tonumeric = FALSE, min = NULL, max = NULL) {
  if(tonumeric) df <- colstonumeric(df)
  df <- (max + min) - df
}
##
## Define function that scores only rows with less than 10% NAs (returns NA if all or above threshold percentage of rows are NA); can reverse code if vector of column indexes and min, max are provided.
ScoreLikert <- function(df, napercent = .1, tonumeric = FALSE, reversecols = NULL, min = NULL, max = NULL) {
  reverse_list <- list(reversecols = reversecols, min = min, max = max)
  reverse_check <- !sapply(reverse_list, is.null)
  
  # Recode to numeric, but watch out to coercion to not introduce NAs
  colstonumeric <- function(df){
    tryCatch({
      df_num <- as.data.frame(
        lapply(df,
               function(x) { as.numeric(as.character(x))})) 
    },warning = function(stop_on_warning) {
      message("Stoped the execution of numeric conversion: ", conditionMessage(stop_on_warning))
    }) 
  }
  
  if(tonumeric) df <- colstonumeric(df)
  
  if(all(reverse_check)){
    df[ ,reversecols] <- (max + min) - df[ ,reversecols]
  }else if(any(reverse_check)){
    stop("Insuficient info for reversing. Please provide: ", paste(names(reverse_list)[!reverse_check], collapse = ", "))
  }
  
  ifelse(rowSums(is.na(df)) > ncol(df) * napercent,
         NA,
         rowSums(df, na.rm = TRUE) * NA ^ (rowSums(!is.na(df)) == 0)
  )
}
##

## Func t test si boxplot simplu
func_t_box <- function(df, ind, pre_var, post_var){
  vars <- c(ind, pre_var, post_var)                # to avoid new tidyverse error of ambiguity due to external vectos
  df_modif <-
    df %>%
    dplyr::select(tidyselect::all_of(vars)) %>% 
    tidyr::drop_na() %>%
    gather(tidyselect::all_of(pre_var), tidyselect::all_of(post_var), key = "Cond", value = "value") %>% 
    mutate_at(vars(c(1, 2)), as.factor) %>% 
    mutate(Cond = factor(Cond, levels = c(pre_var, post_var))) 
  
  stat_comp <- ggpubr::compare_means(value ~ Cond, data = df_modif, method = "t.test", paired = TRUE)
  
  stat_comp2 <-
    df_modif %>% 
    do(tidy(t.test(.$value ~ .$Cond,
                   paired = TRUE,
                   data=.)))
  
  plot <- 
    ggpubr::ggpaired(df_modif, x = "Cond", y = "value", id = ind, 
                     color = "Cond", line.color = "gray", line.size = 0.4,
                     palette = c("#00AFBB", "#FC4E07"), legend = "none") +
      stat_summary(fun.data = mean_se,  colour = "darkred") +
      ggpubr::stat_compare_means(method = "t.test", paired = TRUE, label.x = as.numeric(df_modif$Cond)-0.4, label.y = max(df_modif$value)+0.5) + 
      ggpubr::stat_compare_means(method = "t.test", paired = TRUE, label = "p.signif", comparisons = list(c(pre_var, post_var)))
  
  cat(paste0("#### ", pre_var, " ", post_var, "\n", "\n"))
  print(stat_comp)
  print(stat_comp2)
  print(plot)
}

# library(tidyverse)
# library(ggpubr)
# library(rstatix)
# library(broom)
# library(emmeans)
# library(rlang)


# Function ANCOVAPost
# Takes Long Data
ANCOVAPost_func <- 
  function(data, id_var, 
           time_var, pre_label, post_label,
           value_var = scores, cond_var, 
           assum_check = TRUE, posthoc = TRUE, 
           p_adjust_method = "bonferroni"){
  
  id_var_enq <- rlang::enquo(id_var)
  id_var_name <- rlang::as_name(id_var_enq)    
  time_var_enq <- rlang::enquo(time_var)
  time_var_name <- rlang::as_name(time_var_enq)
  pre_var_enq <- rlang::enquo(pre_label)
  pre_var_name <- rlang::as_name(pre_var_enq)  
  post_var_enq <- rlang::enquo(post_label)
  post_var_name <- rlang::as_name(post_var_enq)
  cond_var_enq <- rlang::enquo(cond_var)
  cond_var_name <- rlang::as_name(cond_var_enq)
  value_var_enq <- rlang::enquo(value_var)
  value_var_name <- rlang::as_name(value_var_enq) 
  
  data_wider <-
    data %>%
    dplyr::select(!!id_var_enq, !!time_var_enq, !!cond_var_enq, !!value_var_enq) %>%
    spread(key = time_var_name, value = value_var_name) # %>%     # if need to compute change score statistics go from here
    # mutate(difference = !!post_var_enq - !!pre_var_enq)  
    
  # Assumptions
  if(assum_check){
  cat("\n Linearity assumptionLinearity assumption (linear relationship between pre-test and post-test for each group) \n")
  # Create a scatter plot between the covariate (i.e., pretest) and the outcome variable (i.e., posttest)
  scatter_lin <-  
    ggscatter(data_wider, x = pre_var_name, y = post_var_name, color = cond_var_name, 
              add = "reg.line", title = "Linearity assumption") +
      stat_regline_equation(aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = !!cond_var_enq))
  
  cat("\n Homogeneity of regression slopes (interaction term is n.s.) \n")
  data_wider %>% 
    anova_test(as.formula(paste0(post_var_name, " ~ ", cond_var_name, " * ", pre_var_name))) %>% 
    print()   
  
  cat("\n Normality of residuals (Model diagnostics & Shapiro Wilk) \n")
  # Fit the model, the covariate goes first
  model <- 
    lm(as.formula(paste0(post_var_name, " ~ ", cond_var_name, " + ", pre_var_name)),
       data = data_wider)
  cat("\n Inspect the model diagnostic metrics \n")
  model.metrics <- 
    augment(model) %>%
    dplyr::select(-.hat, -.sigma, -.fitted, -.se.fit)  %>%    # Remove details
    print()
  cat("\n Normality of residuals (Shapiro Wilk p>.05) \n")
  shapiro_test(model.metrics$.resid) %>% 
    print()
  
  cat("\n Homogeneity of variances (Levene’s test p>.05) \n")
  model.metrics %>% 
    levene_test(as.formula(paste0(".resid", " ~ ", cond_var_name)) ) %>%     
    print()
  
  cat("\n Outliers (needs to be 0) \n")
  model.metrics %>% 
    filter(abs(.std.resid) > 3) %>%
    as.data.frame() %>% 
    print()
  
  }
  
  cat("\n ANCOVAPost \n")
  res_ancova <- 
    data_wider %>% 
    anova_test(as.formula(paste0(post_var_name, " ~ ",  pre_var_name, " + ", cond_var_name)))       # the covariate needs to be first term 
  get_anova_table(res_ancova) %>% print()
  
  cat("\n Pairwise comparisons \n")
  pwc <- 
    data_wider %>% 
    emmeans_test(as.formula(paste0(post_var_name, " ~ ", cond_var_name)),       
                 covariate = !!pre_var_enq,
                 p.adjust.method = p_adjust_method)
  pwc %>% print()
  cat("\n Display the adjusted means of each group, also called as the estimated marginal means (emmeans) \n")
  get_emmeans(pwc) %>% print()
  
  # Visualization: line plots with p-values
  pwc <- 
    pwc %>% 
    add_xy_position(x = cond_var_name, fun = "mean_se")
  
  line_plot <- 
    ggline(get_emmeans(pwc), x = cond_var_name, y = "emmean") +
      geom_errorbar(aes(ymin = conf.low, ymax = conf.high), width = 0.2) + 
      stat_pvalue_manual(pwc, hide.ns = TRUE, tip.length = FALSE) +
      labs(subtitle = get_test_label(res_ancova, detailed = TRUE),
           caption = get_pwc_label(pwc))
  
  if(assum_check){
    list(scatter_lin, line_plot)
  }else{
    line_plot
  }
  
}

# ex.
# ANCOVAPost_func(new_anxiety, time_var = time, pre_label = pretest, post_label = posttest,
#           value_var = scores, cond_var = group, assum_check = TRUE, p_adjust_method = "bonferroni")

# library(tidyverse)
# library(ggpubr)
# library(rstatix)
# library(rlang)

# Define Function for Mixed Anova
tw_mixedANOVA_func <- 
  function(data, id_var, cond_var, time_var, value_var, 
           assum_check = TRUE, posthoc_sig_interac = FALSE, posthoc_ns_interac = FALSE,
           p_adjust_method = "bonferroni"){
    
    # input dataframe needs to have columns names diffrent from "variable" and "value" because it collides with rstatix::shapiro_test
    
    id_var_enq <- rlang::enquo(id_var)  
    cond_var_enq <- rlang::enquo(cond_var)
    cond_var_name <- rlang::as_name(cond_var_enq)
    time_var_enq <- rlang::enquo(time_var)
    time_var_name <- rlang::as_name(time_var_enq)
    value_var_enq <- rlang::enquo(value_var)
    value_var_name <- rlang::as_name(value_var_enq)
    
    data <-                  # need to subset becuase of strange contrasts error in anova_test()
      data %>%
      dplyr::select(!!id_var_enq, !!time_var_enq, !!cond_var_enq, !!value_var_enq) 
    
    # Assumptions
    if(assum_check){
      cat("\n Outliers \n")
      data %>%
        dplyr::group_by(!!time_var_enq, !!cond_var_enq) %>%
        rstatix::identify_outliers(!!value_var_enq) %>%                                  # outliers (needs to be 0)
        print()
      
      cat("\n Normality assumption (p>.05) \n")
      data %>%
        dplyr::group_by(!!time_var_enq, !!cond_var_enq) %>%
        rstatix::shapiro_test(!!value_var_enq) %>%                                        # normality assumption (p>.05)
        print()
      
      qq_plot <- 
        ggpubr::ggqqplot(data = data, value_var_name, ggtheme = theme_bw(), title = "QQ Plot") +
        ggplot2::facet_grid(vars(!!time_var_enq), vars(!!cond_var_enq), labeller = "label_both")    # QQ plot
      
      cat("\n Homogneity of variance assumption - Levene’s test (p>.05) \n")
      data %>%
        group_by(!!time_var_enq ) %>%
        levene_test(as.formula(paste0(value_var_name, " ~ ", cond_var_name))) %>%
        print()
      
      cat("\n Homogeneity of covariances assumption - Box’s test of equality of covariance matrices (p>.001) \n")
      box_m(data = data[, value_var_name, drop = FALSE], group = data[, cond_var_name, drop = TRUE]) %>%
        print
    }
    
    # Two-way rmANOVA - check for interaction (ex. F(2, 22) = 30.4, p < 0.0001)
    cat("\n Two-way rmANOVA \n")
    res_aov <- 
      anova_test(data = data, dv = !!value_var_enq, wid = !!id_var_enq,               # automatically does sphericity Mauchly’s test
                 within = !!time_var_enq, between = !!cond_var_enq)
    get_anova_table(res_aov) %>%  # ges: Greenhouse-Geisser sphericity correction is automatically applied to factors violating the sphericity assumption  
      print()
    
    
    # ------------------------------------------------------------------------
    
    #- Procedure for a significant two-way interaction -
    if(posthoc_sig_interac){
      cat("\n Effect of group at each time point - One-way ANOVA\n")
      one_way <- 
        data %>%
        group_by(!!time_var_enq) %>%
        anova_test(dv = !!value_var_enq, wid = !!id_var_enq, between = !!cond_var_enq) %>%
        get_anova_table() %>%
        adjust_pvalue(method = p_adjust_method)
      one_way %>% print()
      
      cat("\n Pairwise comparisons between group levels \n")
      pwc <- 
        data %>%
        group_by(!!time_var_enq) %>%
        pairwise_t_test(as.formula(paste0(value_var_name, " ~ ", cond_var_name)), 
                        p.adjust.method = p_adjust_method)
      pwc %>% print()
      cat("\n Effect of time at each level of exercises group  - One-way ANOVA \n")
      one_way2 <- 
        data %>%
        group_by(!!cond_var_enq) %>%
        anova_test(dv = !!value_var_enq, wid = !!id_var_enq, within = !!time_var_enq) %>%
        get_anova_table() %>%
        adjust_pvalue(method = p_adjust_method)
      one_way2 %>% print()
      
      cat("\n Pairwise comparisons between time points at each group levels (we have repeated measures by time) \n")
      pwc2 <-
        data %>%
        group_by(!!cond_var_enq) %>%
        pairwise_t_test(
          as.formula(paste0(value_var_name, " ~ ", time_var_name)),     # paste formula, not quosure
          paired = TRUE,
          p.adjust.method = p_adjust_method
        )
      pwc2  %>% print()
    }
    
    #- Procedure for non-significant two-way interaction- 
    # If the interaction is not significant, you need to interpret the main effects for each of the two variables: treatment and time.
    if(posthoc_ns_interac){
      cat("\n Comparisons for treatment variable \n")
      pwc_cond <-
        data %>%
        pairwise_t_test(
          as.formula(paste0(value_var_name, " ~ ", cond_var_name)),     # paste formula, not quosure             
          paired = FALSE,
          p.adjust.method = p_adjust_method
        )
      pwc_cond %>% print()
      cat("\n Comparisons for time variable \n")
      pwc_time <-
        data %>% 
        pairwise_t_test(
          as.formula(paste0(value_var_name, " ~ ", time_var_name)),     # paste formula, not quosure
          paired = TRUE,
          p.adjust.method = p_adjust_method
        )
      pwc_time %>% print()
    }
    
    # Visualization
    bx_plot <- 
      ggboxplot(data, x = time_var_name, y = value_var_name,
                color = cond_var_name, palette = "jco")
    pwc <- 
      pwc %>% 
      add_xy_position(x = time_var_name)
    bx_plot <- 
      bx_plot + 
      stat_pvalue_manual(pwc, tip.length = 0, hide.ns = TRUE) +
      labs(
        subtitle = get_test_label(res_aov, detailed = TRUE),
        caption = get_pwc_label(pwc)
      )
    
    if(assum_check){ 
      list(qq_plot, bx_plot)
    }else{
      bx_plot
    } 
    
  }

# ex. - run on long format
# tw_mixedANOVA_func(data = anxiety, id_var = id, cond_var = group, time_var = time, value_var = score,
#                 posthoc_sig_interac = TRUE, posthoc_ns_interac = TRUE)

2 Read, Clean, Recode

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Read, Clean, Recode, Unite
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Read files
folder <- "C:/Users/Mihai/Desktop/R Notebooks/notebooks/M2-report"
file <- "DateM2 final.xlsx"

setwd(folder)
Data_pre <- rio::import(file.path(folder, file),
                           skip = 3, which = "M2 PRE")

Data_post <- rio::import(file.path(folder, file),
                           skip = 3, which = "M2 POST")



# PRE = colnames(Data_pre); POST = colnames(Data_post) 
# cbind(PRE, POST)                                             # "s1.7" var is missing form POST -- missmatch row 12
#                                                              # also missing "s2.7" and others


# index from "DateM2.xlsx"
# index_ryff_pre <- 94:135
# index_pss_pre <- 136:149
# index_ryff_post <- 94:135 - 5
# index_pss_post <- 136:149 - 5

index_ryff_pre <- 83:124
index_pss_pre <- 125:138
index_ryff_post <- 81:122
index_pss_post <- 123:136


colnames(Data_pre)[1] <- "ID"
colnames(Data_pre)[colnames(Data_pre) == "Stres pre"] <- "VAS_stress_pre"
colnames(Data_pre)[colnames(Data_pre) == "Stres post"] <- "VAS_stress_post"
colnames(Data_pre)[index_ryff_pre] <- sprintf("ryff_%01d", seq(1, 42))
colnames(Data_pre)[index_pss_pre] <- sprintf("pss_%01d", seq(1, 14))
Data_pre <-
  Data_pre %>%
  drop_na(ID) %>%
  dplyr::mutate_if(is.character, list(~dplyr::na_if(., "na"))) 
Data_pre[index_ryff_pre] <- colstonumeric(Data_pre[index_ryff_pre])
Data_pre$pss_8[Data_pre$pss_8 == "++++++"] <- NA                           # typo
Data_pre[index_pss_pre] <- colstonumeric(Data_pre[index_pss_pre])

colnames(Data_post)[1] <- "ID"
colnames(Data_post)[colnames(Data_post) == "Stres pre"] <- "VAS_stress_pre"
colnames(Data_post)[colnames(Data_post) == "Stres post"] <- "VAS_stress_post"
colnames(Data_post)[index_ryff_post] <- sprintf("ryff_%01d", seq(1, 42))
colnames(Data_post)[index_pss_post] <- sprintf("pss_%01d", seq(1, 14))
Data_post <-
  Data_post %>%
  dplyr::mutate_if(is.character, list(~dplyr::na_if(., "na"))) 
Data_post[index_ryff_post] <- colstonumeric(Data_post[index_ryff_post])
Data_post[index_pss_post] <- colstonumeric(Data_post[index_pss_post])

# typos
# check_numeric <- as.data.frame(sapply(Data_pre[index_pss_pre], varhandle::check.numeric)) 
# sapply(check_numeric, function(x) length(which(!x)))

colnames(Data_pre)[colnames(Data_pre) == "s1.1."] <- "s1.1"
colnames(Data_post)[colnames(Data_post) == "s1.1."] <- "s1.1"

Data_post$s2.10[which(Data_post$s2.10 == ".")] <- NA
Data_post$s2.10 <- as.numeric(Data_post$s2.10)
Data_post$s3.16[which(Data_post$s3.16 == "kq")] <- NA
Data_post$s3.16 <- as.numeric(Data_post$s3.16)

## PSS-SF 14 (likert 0-4)
# Items 4, 5, 6, 7, 9, 10, and 13 are scored in reverse direction.

indexitem_revPSS <- c(4, 5, 6, 7, 9, 10, 13)

Data_pre[, index_pss_pre][indexitem_revPSS] <- ReverseCode(Data_pre[, index_pss_pre][indexitem_revPSS], tonumeric = FALSE, min = 0, max = 4)
Data_post[, index_pss_post][indexitem_revPSS] <- ReverseCode(Data_post[, index_pss_post][indexitem_revPSS], tonumeric = FALSE, min = 0, max = 4)

Data_pre$PSS <- ScoreLikert(Data_pre[, index_pss_pre], napercent = .4)
Data_post$PSS <- ScoreLikert(Data_post[, index_pss_post], napercent = .4)
  

## Ryff (likert 1-6)
# Recode negative phrased items:  3,5,10,13,14,15,16,17,18,19,23,26,27,30,31,32,34,36,39,41.
# Autonomy: 1,7,13,19,25,31,37
# Environmental mastery: 2,8,14,20,26,32,38
# Personal Growth: 3,9,15,21,27,33,39
# Positive Relations: 4,10,16,22,28,34,40
# Purpose in life: 5,11,17,23,29,35,41
# Self-acceptance: 6,12,18,24,30,36,42

indexitem_revRYFF <- c(3,5,10,13,14,15,16,17,18,19,23,26,27,30,31,32,34,36,39,41)

Data_pre[, index_ryff_pre][indexitem_revRYFF] <- ReverseCode(Data_pre[, index_ryff_pre][indexitem_revRYFF], tonumeric = FALSE, min = 1, max = 6)
Data_post[, index_ryff_post][indexitem_revRYFF] <- ReverseCode(Data_post[, index_ryff_post][indexitem_revRYFF], tonumeric = FALSE, min = 1, max = 6)

indexitem_Auto <- c(1,7,13,19,25,31,37)
indexitem_EnvM <- c(2,8,14,20,26,32,38)
indexitem_PersG <- c(3,9,15,21,27,33,39)
indexitem_PosRel <- c(4,10,16,22,28,34,40)
indexitem_PurLif <- c(5,11,17,23,29,35,41)
indexitem_SelfAc <- c(6,12,18,24,30,36,42)


Data_pre$Auto <- ScoreLikert(Data_pre[, index_ryff_pre][indexitem_Auto], napercent = .4)
Data_pre$EnvM <- ScoreLikert(Data_pre[, index_ryff_pre][indexitem_EnvM], napercent = .4)
Data_pre$PersG <- ScoreLikert(Data_pre[, index_ryff_pre][indexitem_PersG], napercent = .4)
Data_pre$PosRel <- ScoreLikert(Data_pre[, index_ryff_pre][indexitem_PosRel], napercent = .4)
Data_pre$PurLif <- ScoreLikert(Data_pre[, index_ryff_pre][indexitem_PurLif], napercent = .4)
Data_pre$SelfAc <- ScoreLikert(Data_pre[, index_ryff_pre][indexitem_SelfAc], napercent = .4)

Data_post$Auto <- ScoreLikert(Data_post[, index_ryff_post][indexitem_Auto], napercent = .4)
Data_post$EnvM <- ScoreLikert(Data_post[, index_ryff_post][indexitem_EnvM], napercent = .4)
Data_post$PersG <- ScoreLikert(Data_post[, index_ryff_post][indexitem_PersG], napercent = .4)
Data_post$PosRel <- ScoreLikert(Data_post[, index_ryff_post][indexitem_PosRel], napercent = .4)
Data_post$PurLif <- ScoreLikert(Data_post[, index_ryff_post][indexitem_PurLif], napercent = .4)
Data_post$SelfAc <- ScoreLikert(Data_post[, index_ryff_post][indexitem_SelfAc], napercent = .4)

## Save Scores
# rio::export(Data_pre[, c(1, 150:156)])
# rio::export(Data_post[, c(1, 149:155)])
# nlastcol <- 7
# rio::export(list(PRE = Data_pre[, c(1, (ncol(Data_pre)-nlastcol+1):ncol(Data_pre))], POST = Data_post[, c(1, (ncol(Data_post)-nlastcol+1):ncol(Data_post))]), 
#             "M2 PSS Ryff final.xlsx")

Data_pre$S1_Mean <- rowMeans(Data_pre[, sprintf("s1.%d", c(1:6, 8:16))], na.rm = TRUE)  # Data_pre[, grep("s1.", colnames(Data_pre))]
Data_pre$S2_Mean <- rowMeans(Data_pre[, sprintf("s2.%d", c(1:6, 8:16))], na.rm = TRUE)  # Data_pre[, grep("s2.", colnames(Data_pre))]
Data_pre$S3_Mean <- rowMeans(Data_pre[, sprintf("s3.%d", c(1:6, 8:16))], na.rm = TRUE)  # Data_pre[, grep("s3.", colnames(Data_pre))]

Data_post$S1_Mean <- rowMeans(Data_post[, sprintf("s1.%d", c(1:6, 8:16))], na.rm = TRUE)  # Data_post[, grep("s1.", colnames(Data_post))]
Data_post$S2_Mean <- rowMeans(Data_post[, sprintf("s2.%d", c(1:6, 8:16))], na.rm = TRUE)  # Data_post[, grep("s2.", colnames(Data_post))]
Data_post$S3_Mean <- rowMeans(Data_post[, sprintf("s3.%d", c(1:6, 8:16))], na.rm = TRUE)  # Data_post[, grep("s3.", colnames(Data_post))]

3 Add TR & CTRL groups

tr_ids <- paste0(c(1, 2, 4, 5, 6, 10, 13, 14, 15, 16, 24, 25, 26, 32, 34, 35, 39, 40, 42, 46, 47, 48, 51, 53, 59, 60, 62, 63, 64), " M2") 
ctrl_ids <- paste0(c(3, 7, 8, 9, 11, 12, 17, 18, 19, 20, 21, 22, 23, 27, 28, 30, 33, 36, 37, 38, 41, 43, 45, 49, 52, 54, 55, 56, 57, 58), " M2") 
# Subj from TR: Data_pre[which(Data_pre$ID %in% tr_ids),]  
# Subj from CTRL: Data_pre[which(Data_pre$ID %in% ctrl_ids),] 

Data_pre$Cond <- dplyr::case_when(Data_pre$ID %in% tr_ids ~ "TR",
                                  Data_pre$ID %in% ctrl_ids ~ "CTRL",
                                  TRUE ~ NA_character_)
Data_post$Cond <- dplyr::case_when(Data_post$ID %in% tr_ids ~ "TR",
                                  Data_post$ID %in% ctrl_ids ~ "CTRL",
                                  TRUE ~ NA_character_)

4 Unite data frames

cat("## Number of subjects in pre")

## Number of subjects in pre

Data_pre %>% 
 dplyr::summarise(count = dplyr::n_distinct(ID))

cat("## Number of subjects in post")

## Number of subjects in post

Data_post %>%
 dplyr::summarise(count = dplyr::n_distinct(ID))

Data_pre$PrePost <- rep("Pre", nrow(Data_pre))
Data_post$PrePost <- rep("Post", nrow(Data_post))

Data_pre_scales <- Data_pre[, c("ID", 
                                "VAS_stress_pre", "VAS_stress_post", "PSS", 
                                "Auto", "EnvM", "PersG", "PosRel", "PurLif",  "SelfAc", "S1_Mean", "S2_Mean", "S3_Mean", 
                                "PrePost", "Cond")]
Data_post_scales <- Data_post[, c("ID", 
                                  "VAS_stress_pre", "VAS_stress_post", "PSS", 
                                  "Auto", "EnvM", "PersG", "PosRel", "PurLif",  "SelfAc", "S1_Mean", "S2_Mean", "S3_Mean", 
                                  "PrePost", "Cond")]

Data_unif_long <- rbind(Data_pre_scales, Data_post_scales)
Data_unif_wide <-
  Data_unif_long %>%
  tidyr::pivot_wider(names_from = PrePost, values_from = c(VAS_stress_pre, VAS_stress_post, PSS, Auto, EnvM, PersG, PosRel, PurLif, SelfAc, S1_Mean, S2_Mean, S3_Mean))

Data_unif_long <-
  Data_unif_long %>%
    dplyr::mutate(ID = as.factor(ID),
                  Cond = as.factor(Cond),
                  PrePost = as.factor(PrePost)) %>%
   dplyr::mutate(Vas_Diff = VAS_stress_post - VAS_stress_pre,
                 Vas_Mean = rowMeans(dplyr::select(.data = ., VAS_stress_post, VAS_stress_pre), na.rm = TRUE))

4.1 Export data for RMN in specified order

# rmn_order_ids <- c(1, 14, 13, 6, 4, 5, 10, 15, 2, 16, 42, 34, 24, 25, 26, 35, 39, 40, 32, 59, 53, 64, 46, 47, 62, 63, 48, 51, 60, 
#                    8, 20, 12, 18, 9, 17, 19, 7, 3, 43, 41, 27, 23, 38, 22, 33, 28, 30, 21, 37, 49, 56, 58, 57, 52, 45, 55, 54)
# rmn_order_ids <- paste0(rmn_order_ids, " M2")
# 
# RMN_df <- Data_unif_wide [match(rmn_order_ids, Data_unif_wide $ID),]    # only 57 subjs for RMN
# 
# rio::export(RMN_df, "RMN ordered M2.xlsx")

cat("### PSS")
func_t_box(Data_unif_wide, "ID", "PSS_Pre", "PSS_Post")

cat("### Ryff")
func_t_box(Data_unif_wide, "ID", "Auto_Pre", "Auto_Post")
func_t_box(Data_unif_wide, "ID", "EnvM_Pre", "EnvM_Post")
func_t_box(Data_unif_wide, "ID", "PersG_Pre", "PersG_Post")
func_t_box(Data_unif_wide, "ID", "PosRel_Pre", "PosRel_Post")
func_t_box(Data_unif_wide, "ID", "PurLif_Pre", "PurLif_Post")
func_t_box(Data_unif_wide, "ID", "SelfAc_Pre", "SelfAc_Post")


cat("### Memory Scales")
func_t_box(Data_unif_wide, "ID", "S1_Mean_Pre", "S1_Mean_Post")
func_t_box(Data_unif_wide, "ID", "S2_Mean_Pre", "S2_Mean_Post")
func_t_box(Data_unif_wide, "ID", "S3_Mean_Pre", "S3_Mean_Post")

–>

5 ANCOVA Post

cat("## PSS")

5.1 PSS

ANCOVAPost_func(data = Data_unif_long, id_var = ID, time_var = PrePost, pre_label = Pre, post_label = Post,
                value_var = PSS, cond_var = Cond, assum_check = TRUE, p_adjust_method = "bonferroni")

Linearity assumptionLinearity assumption (linear relationship between pre-test and post-test for each group)

Homogeneity of regression slopes (interaction term is n.s.) ANOVA Table (type II tests)

Effect DFn DFd      F           p p<.05      ges

1 Cond 1 55 0.012 0.914000000 0.000215 2 Pre 1 55 33.434 0.000000361 * 0.378000 3 Cond:Pre 1 55 2.739 0.104000000 0.047000

Normality of residuals (Model diagnostics & Shapiro Wilk)

Inspect the model diagnostic metrics

Normality of residuals (Shapiro Wilk p>.05)

Homogeneity of variances (Levene’s test p>.05)

Outliers (needs to be 0)

ANCOVAPost ANOVA Table (type II tests)

Effect DFn DFd F p p<.05 ges 1 Pre 1 56 32.428 0.000000475 * 0.367000 2 Cond 1 56 0.011 0.915000000 0.000205

Pairwise comparisons

Display the adjusted means of each group, also called as the estimated marginal means (emmeans)

[[1]]

[[2]]

cat("## Auto")

5.2 Auto

ANCOVAPost_func(data = Data_unif_long, id_var = ID, time_var = PrePost, pre_label = Pre, post_label = Post,
                value_var = Auto, cond_var = Cond, assum_check = TRUE, p_adjust_method = "bonferroni")

Linearity assumptionLinearity assumption (linear relationship between pre-test and post-test for each group)

Homogeneity of regression slopes (interaction term is n.s.) ANOVA Table (type II tests)

Effect DFn DFd          F                p p<.05        ges

1 Cond 1 55 0.0410000 0.84100000000000 0.00073700 2 Pre 1 55 74.0980000 0.00000000000901 * 0.57400000 3 Cond:Pre 1 55 0.0000569 0.99400000000000 0.00000104

Normality of residuals (Model diagnostics & Shapiro Wilk)

Inspect the model diagnostic metrics

Normality of residuals (Shapiro Wilk p>.05)

Homogeneity of variances (Levene’s test p>.05)

Outliers (needs to be 0)

ANCOVAPost ANOVA Table (type II tests)

Effect DFn DFd F p p<.05 ges 1 Pre 1 56 75.445 0.00000000000583 * 0.574000 2 Cond 1 56 0.041 0.84000000000000 0.000737

Pairwise comparisons

Display the adjusted means of each group, also called as the estimated marginal means (emmeans)

[[1]]

[[2]]

cat("## EnvM")

5.3 EnvM

ANCOVAPost_func(data = Data_unif_long, id_var = ID, time_var = PrePost, pre_label = Pre, post_label = Post,
                value_var = EnvM, cond_var = Cond, assum_check = TRUE, p_adjust_method = "bonferroni")

Linearity assumptionLinearity assumption (linear relationship between pre-test and post-test for each group)

Homogeneity of regression slopes (interaction term is n.s.) ANOVA Table (type II tests)

Effect DFn DFd      F           p p<.05      ges

1 Cond 1 55 0.018 0.895000000 0.000321 2 Pre 1 55 34.023 0.000000299 * 0.382000 3 Cond:Pre 1 55 3.189 0.080000000 0.055000

Normality of residuals (Model diagnostics & Shapiro Wilk)

Inspect the model diagnostic metrics

Normality of residuals (Shapiro Wilk p>.05)

Homogeneity of variances (Levene’s test p>.05)

Outliers (needs to be 0)

ANCOVAPost ANOVA Table (type II tests)

Effect DFn DFd F p p<.05 ges 1 Pre 1 56 32.743 0.000000429 * 0.369000 2 Cond 1 56 0.017 0.897000000 0.000303

Pairwise comparisons

Display the adjusted means of each group, also called as the estimated marginal means (emmeans)

[[1]]

[[2]]

cat("## PersG")

5.4 PersG

ANCOVAPost_func(data = Data_unif_long, id_var = ID, time_var = PrePost, pre_label = Pre, post_label = Post,
                value_var = PersG, cond_var = Cond, assum_check = TRUE, p_adjust_method = "bonferroni")

Linearity assumptionLinearity assumption (linear relationship between pre-test and post-test for each group)

Homogeneity of regression slopes (interaction term is n.s.) ANOVA Table (type II tests)

Effect DFn DFd      F          p p<.05      ges

1 Cond 1 55 0.053 0.81800000 0.000968 2 Pre 1 55 29.116 0.00000149 * 0.346000 3 Cond:Pre 1 55 0.731 0.39600000 0.013000

Normality of residuals (Model diagnostics & Shapiro Wilk)

Inspect the model diagnostic metrics

Normality of residuals (Shapiro Wilk p>.05)

Homogeneity of variances (Levene’s test p>.05)

Outliers (needs to be 0)

ANCOVAPost ANOVA Table (type II tests)

Effect DFn DFd F p p<.05 ges 1 Pre 1 56 29.257 0.00000136 * 0.343000 2 Cond 1 56 0.054 0.81800000 0.000955

Pairwise comparisons

Display the adjusted means of each group, also called as the estimated marginal means (emmeans)

[[1]]

[[2]]

cat("## PosRel")

5.5 PosRel

ANCOVAPost_func(data = Data_unif_long, id_var = ID, time_var = PrePost, pre_label = Pre, post_label = Post,
                value_var = PosRel, cond_var = Cond, assum_check = TRUE, p_adjust_method = "bonferroni")

Linearity assumptionLinearity assumption (linear relationship between pre-test and post-test for each group)

Homogeneity of regression slopes (interaction term is n.s.) ANOVA Table (type II tests)

Effect DFn DFd      F               p p<.05   ges

1 Cond 1 55 0.092 0.7620000000000 0.002 2 Pre 1 55 64.079 0.0000000000856 * 0.538 3 Cond:Pre 1 55 1.048 0.3110000000000 0.019

Normality of residuals (Model diagnostics & Shapiro Wilk)

Inspect the model diagnostic metrics

Normality of residuals (Shapiro Wilk p>.05)

Homogeneity of variances (Levene’s test p>.05)

Outliers (needs to be 0)

ANCOVAPost ANOVA Table (type II tests)

Effect DFn DFd F p p<.05 ges 1 Pre 1 56 64.024 0.0000000000769 * 0.533 2 Cond 1 56 0.092 0.7620000000000 0.002

Pairwise comparisons

Display the adjusted means of each group, also called as the estimated marginal means (emmeans)

[[1]]

[[2]]

cat("## PurLif")

5.6 PurLif

ANCOVAPost_func(data = Data_unif_long, id_var = ID, time_var = PrePost, pre_label = Pre, post_label = Post,
                value_var = PurLif, cond_var = Cond, assum_check = TRUE, p_adjust_method = "bonferroni")

Linearity assumptionLinearity assumption (linear relationship between pre-test and post-test for each group)

Homogeneity of regression slopes (interaction term is n.s.) ANOVA Table (type II tests)

Effect DFn DFd          F           p p<.05         ges

1 Cond 1 55 0.2230000 0.639000000 0.004000000 2 Pre 1 55 37.4200000 0.000000104 * 0.405000000 3 Cond:Pre 1 55 0.0000161 0.997000000 0.000000293

Normality of residuals (Model diagnostics & Shapiro Wilk)

Inspect the model diagnostic metrics

Normality of residuals (Shapiro Wilk p>.05)

Homogeneity of variances (Levene’s test p>.05)

Outliers (needs to be 0)

ANCOVAPost ANOVA Table (type II tests)

Effect DFn DFd F p p<.05 ges 1 Pre 1 56 38.101 0.0000000795 * 0.405 2 Cond 1 56 0.227 0.6360000000 0.004

Pairwise comparisons

Display the adjusted means of each group, also called as the estimated marginal means (emmeans)

[[1]]

[[2]]

cat("## SelfAc")

5.7 SelfAc

ANCOVAPost_func(data = Data_unif_long, id_var = ID, time_var = PrePost, pre_label = Pre, post_label = Post,
                value_var = SelfAc, cond_var = Cond, assum_check = TRUE, p_adjust_method = "bonferroni")

Linearity assumptionLinearity assumption (linear relationship between pre-test and post-test for each group)

Homogeneity of regression slopes (interaction term is n.s.) ANOVA Table (type II tests)

Effect DFn DFd      F               p p<.05       ges

1 Cond 1 55 0.005 0.9430000000000 0.0000941 2 Pre 1 55 69.887 0.0000000000227 * 0.5600000 3 Cond:Pre 1 55 1.004 0.3210000000000 0.0180000

Normality of residuals (Model diagnostics & Shapiro Wilk)

Inspect the model diagnostic metrics

Normality of residuals (Shapiro Wilk p>.05)

Homogeneity of variances (Levene’s test p>.05)

Outliers (needs to be 0)

ANCOVAPost ANOVA Table (type II tests)

Effect DFn DFd F p p<.05 ges 1 Pre 1 56 69.881 0.0000000000199 * 0.5550000 2 Cond 1 56 0.005 0.9430000000000 0.0000924

Pairwise comparisons

Display the adjusted means of each group, also called as the estimated marginal means (emmeans)

[[1]]

[[2]]

cat("## S1_Mean")

5.8 S1_Mean

ANCOVAPost_func(data = Data_unif_long, id_var = ID, time_var = PrePost, pre_label = Pre, post_label = Post,
                value_var = S1_Mean, cond_var = Cond, assum_check = TRUE, p_adjust_method = "bonferroni")

Linearity assumptionLinearity assumption (linear relationship between pre-test and post-test for each group)

Homogeneity of regression slopes (interaction term is n.s.) ANOVA Table (type II tests)

Effect DFn DFd      F               p p<.05      ges

1 Cond 1 55 0.024 0.8780000000000 0.000431 2 Pre 1 55 72.493 0.0000000000128 * 0.569000 3 Cond:Pre 1 55 0.022 0.8830000000000 0.000400

Normality of residuals (Model diagnostics & Shapiro Wilk)

Inspect the model diagnostic metrics

Normality of residuals (Shapiro Wilk p>.05)

Homogeneity of variances (Levene’s test p>.05)

Outliers (needs to be 0)

ANCOVAPost ANOVA Table (type II tests)

Effect DFn DFd F p p<.05 ges 1 Pre 1 56 73.781 0.00000000000836 * 0.569000 2 Cond 1 56 0.024 0.87700000000000 0.000431

Pairwise comparisons

Display the adjusted means of each group, also called as the estimated marginal means (emmeans)

[[1]]

[[2]]

cat("## S2_Mean")

5.9 S2_Mean

ANCOVAPost_func(data = Data_unif_long, id_var = ID, time_var = PrePost, pre_label = Pre, post_label = Post,
                value_var = S2_Mean, cond_var = Cond, assum_check = TRUE, p_adjust_method = "bonferroni")

Linearity assumptionLinearity assumption (linear relationship between pre-test and post-test for each group)

Homogeneity of regression slopes (interaction term is n.s.) ANOVA Table (type II tests)

Effect DFn DFd      F                p p<.05   ges

1 Cond 1 55 2.077 0.15500000000000 0.036 2 Pre 1 55 74.964 0.00000000000748 * 0.577 3 Cond:Pre 1 55 1.310 0.25700000000000 0.023

Normality of residuals (Model diagnostics & Shapiro Wilk)

Inspect the model diagnostic metrics

Normality of residuals (Shapiro Wilk p>.05)

Homogeneity of variances (Levene’s test p>.05)

Outliers (needs to be 0)

ANCOVAPost ANOVA Table (type II tests)

Effect DFn DFd F p p<.05 ges 1 Pre 1 56 74.551 0.00000000000707 * 0.571 2 Cond 1 56 2.066 0.15600000000000 0.036

Pairwise comparisons

Display the adjusted means of each group, also called as the estimated marginal means (emmeans)

[[1]]

[[2]]

cat("## S3_Mean")

5.10 S3_Mean

ANCOVAPost_func(data = Data_unif_long, id_var = ID, time_var = PrePost, pre_label = Pre, post_label = Post,
                value_var = S3_Mean, cond_var = Cond, assum_check = TRUE, p_adjust_method = "bonferroni")

Linearity assumptionLinearity assumption (linear relationship between pre-test and post-test for each group)

Homogeneity of regression slopes (interaction term is n.s.) ANOVA Table (type II tests)

Effect DFn DFd      F                 p p<.05   ges

1 Cond 1 55 3.145 0.082000000000000 0.054 2 Pre 1 55 93.246 0.000000000000192 * 0.629 3 Cond:Pre 1 55 0.153 0.697000000000000 0.003

Normality of residuals (Model diagnostics & Shapiro Wilk)

Inspect the model diagnostic metrics

Normality of residuals (Shapiro Wilk p>.05)

Homogeneity of variances (Levene’s test p>.05)

Outliers (needs to be 0)

ANCOVAPost ANOVA Table (type II tests)

Effect DFn DFd F p p<.05 ges 1 Pre 1 56 94.678 0.000000000000122 * 0.628 2 Cond 1 56 3.193 0.079000000000000 0.054

Pairwise comparisons

Display the adjusted means of each group, also called as the estimated marginal means (emmeans)

[[1]]

[[2]]

cat("## Vas_Diff")

5.11 Vas_Diff

ANCOVAPost_func(data = Data_unif_long, id_var = ID, time_var = PrePost, pre_label = Pre, post_label = Post,
                value_var = Vas_Diff, cond_var = Cond, assum_check = TRUE, p_adjust_method = "bonferroni")

Linearity assumptionLinearity assumption (linear relationship between pre-test and post-test for each group)

Homogeneity of regression slopes (interaction term is n.s.) ANOVA Table (type II tests)

Effect DFn DFd     F     p p<.05   ges

1 Cond 1 53 0.362 0.550 0.007 2 Pre 1 53 9.515 0.003 * 0.152 3 Cond:Pre 1 53 1.008 0.320 0.019

Normality of residuals (Model diagnostics & Shapiro Wilk)

Inspect the model diagnostic metrics

Normality of residuals (Shapiro Wilk p>.05)

Homogeneity of variances (Levene’s test p>.05)

Outliers (needs to be 0)

ANCOVAPost ANOVA Table (type II tests)

Effect DFn DFd F p p<.05 ges 1 Pre 1 54 9.514 0.003 * 0.150 2 Cond 1 54 0.362 0.550 0.007

Pairwise comparisons

Display the adjusted means of each group, also called as the estimated marginal means (emmeans)

[[1]]

[[2]]

cat("## Vas_Mean")

5.12 Vas_Mean

ANCOVAPost_func(data = Data_unif_long, id_var = ID, time_var = PrePost, pre_label = Pre, post_label = Post,
                value_var = Vas_Mean, cond_var = Cond, assum_check = TRUE, p_adjust_method = "bonferroni")

Linearity assumptionLinearity assumption (linear relationship between pre-test and post-test for each group)

Homogeneity of regression slopes (interaction term is n.s.) ANOVA Table (type II tests)

Effect DFn DFd      F        p p<.05      ges

1 Cond 1 55 0.022 0.884000 0.000392 2 Pre 1 55 15.386 0.000245 * 0.219000 3 Cond:Pre 1 55 0.257 0.614000 0.005000

Normality of residuals (Model diagnostics & Shapiro Wilk)

Inspect the model diagnostic metrics

Normality of residuals (Shapiro Wilk p>.05)

Homogeneity of variances (Levene’s test p>.05)

Outliers (needs to be 0)

ANCOVAPost ANOVA Table (type II tests)

Effect DFn DFd F p p<.05 ges 1 Pre 1 56 15.593 0.000222 * 0.21800 2 Cond 1 56 0.022 0.883000 0.00039

Pairwise comparisons

Display the adjusted means of each group, also called as the estimated marginal means (emmeans)

[[1]]

[[2]]

6 Mixed ANOVA

cat("## PSS")

6.1 PSS

tw_mixedANOVA_func(data = Data_unif_long, id_var = ID, cond_var = Cond, time_var = PrePost, 
                   value_var = PSS, posthoc_sig_interac = TRUE, posthoc_ns_interac = TRUE)

Outliers

Normality assumption (p>.05)

Homogneity of variance assumption - Levene’s test (p>.05)

Homogeneity of covariances assumption - Box’s test of equality of covariance matrices (p>.001)

Two-way rmANOVA ANOVA Table (type III tests)

    Effect DFn DFd     F     p p<.05      ges

1 Cond 1 57 0.918 0.342 0.013000 2 PrePost 1 57 1.092 0.300 0.004000 3 Cond:PrePost 1 57 0.229 0.634 0.000801

Effect of group at each time point - One-way ANOVA

Pairwise comparisons between group levels

Effect of time at each level of exercises group - One-way ANOVA

Pairwise comparisons between time points at each group levels (we have repeated measures by time)

Comparisons for treatment variable

Comparisons for time variable

[[1]]

[[2]]

cat("## Auto")

6.2 Auto

tw_mixedANOVA_func(data = Data_unif_long, id_var = ID, cond_var = Cond, time_var = PrePost, 
                   value_var = Auto, posthoc_sig_interac = TRUE, posthoc_ns_interac = TRUE)

Outliers

Normality assumption (p>.05)

Homogneity of variance assumption - Levene’s test (p>.05)

Homogeneity of covariances assumption - Box’s test of equality of covariance matrices (p>.001)

Two-way rmANOVA ANOVA Table (type III tests)

    Effect DFn DFd     F     p p<.05        ges

1 Cond 1 57 0.513 0.477 0.00800000 2 PrePost 1 57 0.493 0.485 0.00100000 3 Cond:PrePost 1 57 0.003 0.954 0.00000739

Effect of group at each time point - One-way ANOVA

Pairwise comparisons between group levels

Effect of time at each level of exercises group - One-way ANOVA

Pairwise comparisons between time points at each group levels (we have repeated measures by time)

Comparisons for treatment variable

Comparisons for time variable

[[1]]

[[2]]

cat("## EnvM")

6.3 EnvM

tw_mixedANOVA_func(data = Data_unif_long, id_var = ID, cond_var = Cond, time_var = PrePost, 
                   value_var = EnvM, posthoc_sig_interac = TRUE, posthoc_ns_interac = TRUE)

Outliers

Normality assumption (p>.05)

Homogneity of variance assumption - Levene’s test (p>.05)

Homogeneity of covariances assumption - Box’s test of equality of covariance matrices (p>.001)

Two-way rmANOVA ANOVA Table (type III tests)

    Effect DFn DFd     F     p p<.05      ges

1 Cond 1 57 0.227 0.636 0.003000 2 PrePost 1 57 0.932 0.338 0.003000 3 Cond:PrePost 1 57 0.099 0.754 0.000346

Effect of group at each time point - One-way ANOVA

Pairwise comparisons between group levels

Effect of time at each level of exercises group - One-way ANOVA

Pairwise comparisons between time points at each group levels (we have repeated measures by time)

Comparisons for treatment variable

Comparisons for time variable

[[1]]

[[2]]

cat("## PersG")

6.4 PersG

tw_mixedANOVA_func(data = Data_unif_long, id_var = ID, cond_var = Cond, time_var = PrePost, 
                   value_var = PersG, posthoc_sig_interac = TRUE, posthoc_ns_interac = TRUE)

Outliers

Normality assumption (p>.05)

Homogneity of variance assumption - Levene’s test (p>.05)

Homogeneity of covariances assumption - Box’s test of equality of covariance matrices (p>.001)

Two-way rmANOVA ANOVA Table (type III tests)

    Effect DFn DFd     F     p p<.05       ges

1 Cond 1 57 0.134 0.716 0.0020000 2 PrePost 1 57 1.153 0.287 0.0040000 3 Cond:PrePost 1 57 0.014 0.908 0.0000496

Effect of group at each time point - One-way ANOVA

Pairwise comparisons between group levels

Effect of time at each level of exercises group - One-way ANOVA

Pairwise comparisons between time points at each group levels (we have repeated measures by time)

Comparisons for treatment variable

Comparisons for time variable

[[1]]

[[2]]

cat("## PosRel")

6.5 PosRel

tw_mixedANOVA_func(data = Data_unif_long, id_var = ID, cond_var = Cond, time_var = PrePost, 
                   value_var = PosRel, posthoc_sig_interac = TRUE, posthoc_ns_interac = TRUE)

Outliers

Normality assumption (p>.05)

Homogneity of variance assumption - Levene’s test (p>.05)

Homogeneity of covariances assumption - Box’s test of equality of covariance matrices (p>.001)

Two-way rmANOVA ANOVA Table (type III tests)

    Effect DFn DFd     F     p p<.05      ges

1 Cond 1 57 0.037 0.847 0.000563 2 PrePost 1 57 0.236 0.629 0.000584 3 Cond:PrePost 1 57 0.124 0.726 0.000308

Effect of group at each time point - One-way ANOVA

Pairwise comparisons between group levels

Effect of time at each level of exercises group - One-way ANOVA

Pairwise comparisons between time points at each group levels (we have repeated measures by time)

Comparisons for treatment variable

Comparisons for time variable

[[1]]

[[2]]

cat("## PurLif")

6.6 PurLif

tw_mixedANOVA_func(data = Data_unif_long, id_var = ID, cond_var = Cond, time_var = PrePost, 
                   value_var = PurLif, posthoc_sig_interac = TRUE, posthoc_ns_interac = TRUE)

Outliers

Normality assumption (p>.05)

Homogneity of variance assumption - Levene’s test (p>.05)

Homogeneity of covariances assumption - Box’s test of equality of covariance matrices (p>.001)

Two-way rmANOVA ANOVA Table (type III tests)

    Effect DFn DFd     F     p p<.05      ges

1 Cond 1 57 0.678 0.414 0.010000 2 PrePost 1 57 0.286 0.595 0.000916 3 Cond:PrePost 1 57 0.042 0.838 0.000134

Effect of group at each time point - One-way ANOVA

Pairwise comparisons between group levels

Effect of time at each level of exercises group - One-way ANOVA

Pairwise comparisons between time points at each group levels (we have repeated measures by time)

Comparisons for treatment variable

Comparisons for time variable

[[1]]

[[2]]

cat("## SelfAc")

6.7 SelfAc

tw_mixedANOVA_func(data = Data_unif_long, id_var = ID, cond_var = Cond, time_var = PrePost, 
                   value_var = SelfAc, posthoc_sig_interac = TRUE, posthoc_ns_interac = TRUE)

Outliers

Normality assumption (p>.05)

Homogneity of variance assumption - Levene’s test (p>.05)

Homogeneity of covariances assumption - Box’s test of equality of covariance matrices (p>.001)

Two-way rmANOVA ANOVA Table (type III tests)

    Effect DFn DFd     F     p p<.05      ges

1 Cond 1 57 1.128 0.293 0.017000 2 PrePost 1 57 0.960 0.331 0.002000 3 Cond:PrePost 1 57 0.205 0.653 0.000458

Effect of group at each time point - One-way ANOVA

Pairwise comparisons between group levels

Effect of time at each level of exercises group - One-way ANOVA

Pairwise comparisons between time points at each group levels (we have repeated measures by time)

Comparisons for treatment variable

Comparisons for time variable

[[1]]

[[2]]

cat("## S1_Mean")

6.8 S1_Mean

tw_mixedANOVA_func(data = Data_unif_long, id_var = ID, cond_var = Cond, time_var = PrePost, 
                   value_var = S1_Mean, posthoc_sig_interac = TRUE, posthoc_ns_interac = TRUE)

Outliers

Normality assumption (p>.05)

Homogneity of variance assumption - Levene’s test (p>.05)

Homogeneity of covariances assumption - Box’s test of equality of covariance matrices (p>.001)

Two-way rmANOVA ANOVA Table (type III tests)

    Effect DFn DFd      F     p p<.05      ges

1 Cond 1 57 2.845 0.097 0.042000 2 PrePost 1 57 11.041 0.002 * 0.023000 3 Cond:PrePost 1 57 0.118 0.732 0.000256

Effect of group at each time point - One-way ANOVA

Pairwise comparisons between group levels

Effect of time at each level of exercises group - One-way ANOVA

Pairwise comparisons between time points at each group levels (we have repeated measures by time)

Comparisons for treatment variable

Comparisons for time variable

[[1]]

[[2]]

cat("## S2_Mean")

6.9 S2_Mean

tw_mixedANOVA_func(data = Data_unif_long, id_var = ID, cond_var = Cond, time_var = PrePost, 
                   value_var = S2_Mean, posthoc_sig_interac = TRUE, posthoc_ns_interac = TRUE)

Outliers

Normality assumption (p>.05)

Homogneity of variance assumption - Levene’s test (p>.05)

Homogeneity of covariances assumption - Box’s test of equality of covariance matrices (p>.001)

Two-way rmANOVA ANOVA Table (type III tests)

    Effect DFn DFd      F         p p<.05   ges

1 Cond 1 57 2.952 0.0910000 0.043 2 PrePost 1 57 17.549 0.0000983 * 0.037 3 Cond:PrePost 1 57 1.569 0.2150000 0.003

Effect of group at each time point - One-way ANOVA

Pairwise comparisons between group levels

Effect of time at each level of exercises group - One-way ANOVA

Pairwise comparisons between time points at each group levels (we have repeated measures by time)

Comparisons for treatment variable

Comparisons for time variable

[[1]]

[[2]]

cat("## S3_Mean")

6.10 S3_Mean

tw_mixedANOVA_func(data = Data_unif_long, id_var = ID, cond_var = Cond, time_var = PrePost, 
                   value_var = S3_Mean, posthoc_sig_interac = TRUE, posthoc_ns_interac = TRUE)

Outliers

Normality assumption (p>.05)

Homogneity of variance assumption - Levene’s test (p>.05)

Homogeneity of covariances assumption - Box’s test of equality of covariance matrices (p>.001)

Two-way rmANOVA ANOVA Table (type III tests)

    Effect DFn DFd     F     p p<.05   ges

1 Cond 1 57 1.267 0.265 0.019 2 PrePost 1 57 3.754 0.058 0.007 3 Cond:PrePost 1 57 1.881 0.176 0.003

Effect of group at each time point - One-way ANOVA

Pairwise comparisons between group levels

Effect of time at each level of exercises group - One-way ANOVA

Pairwise comparisons between time points at each group levels (we have repeated measures by time)

Comparisons for treatment variable

Comparisons for time variable

[[1]]

[[2]]

cat("## Vas_Diff")

6.11 Vas_Diff

tw_mixedANOVA_func(data = Data_unif_long, id_var = ID, cond_var = Cond, time_var = PrePost, 
                   value_var = Vas_Diff, posthoc_sig_interac = TRUE, posthoc_ns_interac = TRUE)

Outliers

Normality assumption (p>.05)

Homogneity of variance assumption - Levene’s test (p>.05)

Homogeneity of covariances assumption - Box’s test of equality of covariance matrices (p>.001)

Two-way rmANOVA ANOVA Table (type III tests)

    Effect DFn DFd       F     p p<.05         ges

1 Cond 1 55 1.01900 0.317 0.013000000 2 PrePost 1 55 9.60500 0.003 * 0.051000000 3 Cond:PrePost 1 55 0.00007 0.993 0.000000391

Effect of group at each time point - One-way ANOVA

Pairwise comparisons between group levels

Effect of time at each level of exercises group - One-way ANOVA

Pairwise comparisons between time points at each group levels (we have repeated measures by time)

Comparisons for treatment variable

Comparisons for time variable

[[1]]

[[2]]

cat("## Vas_Mean")

6.12 Vas_Mean

tw_mixedANOVA_func(data = Data_unif_long, id_var = ID, cond_var = Cond, time_var = PrePost, 
                   value_var = Vas_Mean, posthoc_sig_interac = TRUE, posthoc_ns_interac = TRUE)

Outliers

Normality assumption (p>.05)

Homogneity of variance assumption - Levene’s test (p>.05)

Homogeneity of covariances assumption - Box’s test of equality of covariance matrices (p>.001)

Two-way rmANOVA ANOVA Table (type III tests)

    Effect DFn DFd     F     p p<.05      ges

1 Cond 1 57 0.459 0.501 0.006000 2 PrePost 1 57 0.379 0.541 0.002000 3 Cond:PrePost 1 57 0.029 0.866 0.000136

Effect of group at each time point - One-way ANOVA

Pairwise comparisons between group levels

Effect of time at each level of exercises group - One-way ANOVA

Pairwise comparisons between time points at each group levels (we have repeated measures by time)

Comparisons for treatment variable

Comparisons for time variable

[[1]]

[[2]]

7 Session Info

R version 3.6.1 (2019-07-05)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 8.1 x64 (build 9600)

Matrix products: default

locale:
[1] LC_COLLATE=Romanian_Romania.1250  LC_CTYPE=Romanian_Romania.1250    LC_MONETARY=Romanian_Romania.1250 LC_NUMERIC=C                     
[5] LC_TIME=Romanian_Romania.1250    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] rlang_0.4.6                emmeans_1.4.5              rio_0.5.16                 scales_1.1.0               ggpubr_0.2.5               magrittr_1.5              
 [7] tadaatoolbox_0.16.1        summarytools_0.8.8         rstatix_0.5.0              broom_0.5.6                PerformanceAnalytics_1.5.2 xts_0.11-2                
[13] zoo_1.8-4                  psych_1.9.12.31            forcats_0.5.0              stringr_1.4.0              dplyr_0.8.5                purrr_0.3.3               
[19] readr_1.3.1                tidyr_1.0.2                tibble_3.0.0               ggplot2_3.3.0              tidyverse_1.3.0            papaja_0.1.0.9842         
[25] pacman_0.5.1              

loaded via a namespace (and not attached):
 [1] TH.data_1.0-9      colorspace_1.4-1   ggsignif_0.4.0     pryr_0.1.4         ellipsis_0.3.0     estimability_1.3   fs_1.4.1           rstudioapi_0.11   
 [9] farver_2.0.3       fansi_0.4.1        mvtnorm_1.1-0      lubridate_1.7.4    xml2_1.3.1         codetools_0.2-16   splines_3.6.1      mnormt_1.5-6      
[17] knitr_1.28         pixiedust_0.8.6    polynom_1.3-9      jsonlite_1.6.1     dbplyr_1.4.3       compiler_3.6.1     httr_1.4.1         backports_1.1.6   
[25] assertthat_0.2.1   Matrix_1.2-17      cli_2.0.2          htmltools_0.4.0    tools_3.6.1        coda_0.19-2        gtable_0.3.0       glue_1.4.0        
[33] Rcpp_1.0.4.6       carData_3.0-2      cellranger_1.1.0   vctrs_0.2.4        nlme_3.1-140       xfun_0.13          openxlsx_4.1.0     rvest_0.3.5       
[41] lifecycle_0.2.0    MASS_7.3-51.4      hms_0.5.3          parallel_3.6.1     sandwich_2.5-0     expm_0.999-3       pwr_1.2-2          curl_4.3          
[49] gridExtra_2.3      pander_0.6.3       stringi_1.4.6      nortest_1.0-4      boot_1.3-22        zip_1.0.0          pkgconfig_2.0.3    matrixStats_0.54.0
[57] bitops_1.0-6       lattice_0.20-38    labeling_0.3       rapportools_1.0    tidyselect_1.0.0   ggsci_2.9          plyr_1.8.6         R6_2.4.1          
[65] DescTools_0.99.34  generics_0.0.2     multcomp_1.4-8     DBI_1.0.0          pillar_1.4.3       haven_2.2.0        foreign_0.8-71     withr_2.1.2       
[73] mgcv_1.8-28        survival_2.44-1.1  abind_1.4-5        RCurl_1.95-4.11    modelr_0.1.6       crayon_1.3.4       car_3.0-7          viridis_0.5.1     
[81] grid_3.6.1         readxl_1.3.1       data.table_1.12.8  reprex_0.3.0       digest_0.6.25      xtable_1.8-4       munsell_0.5.0      viridisLite_0.3.0 
[89] quadprog_1.5-5    

 

---

A work by Claudiu Papasteri

claudiu.papasteri@gmail.com